How should I practice Assign Student Grades from CSV Marks?

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Assign Student Grades from CSV Marks

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Problem statement

You receive student marks from a comma-separated CSV with header student_id,marks. Each record has one unique student ID and one integer mark.

The practice runner has already parsed that CSV into the students dataframe using the displayed schema. Implement assign_grades(students) to assign a letter grade to every row. CSV loading is context for this exercise; the judged operation is the column transformation on the supplied dataframe.

Use these grade bands:

Mark	Grade
`90..100`	`A`
`80..89`	`B`
`70..79`	`C`
`60..69`	`D`
`0..59`	`F`

Return a dataframe with columns student_id, marks, and grade in that order. Retain every original ID and mark and preserve the CSV row order. Equal marks still belong to separate students. Do not average marks, sort or group students, or round marks.

A header-only CSV is valid and returns an empty dataframe with the same three result columns.

Table schema

Pandas

Use the same input data with any supported language. Open the Schema tab in the editor to see the generated SQL setup or Pandas DataFrames.

students

Records parsed from CSV columns student_id,marks in their original row order.

Column	Type	Nullable	Description
student_idPK	Integer	No	Unique student identifier; preserves the identity of each CSV row.
marks	Integer	No	One mark per student, from 0 through 100 inclusive.

Expected result

Your query or function must return these columns.

Column	Type	Nullable	Description
student_id	Integer	No	Unique student identifier; preserves the identity of each CSV row.
marks	Integer	No	One mark per student, from 0 through 100 inclusive.
grade	Text	No	A, B, C, D or F assigned by the displayed integer mark bands.

Row order: must match exactly. Numeric tolerance: 0.

Constraints

The CSV has header student_id,marks and from 0 through 1000 data rows.
Student IDs are unique integers from 1 through 10^9.
Every mark is an integer from 0 through 100; neither field is null.
Each record already contains the single mark used for its grade; no subject aggregation or rounding is required.